{
  "id": 14851239,
  "title": "Uncertainty Quantification in Pairwise Difference Learning for Classification",
  "url": "https://urgent.news/2026/10/15/uncertainty-quantification-in-pairwise-difference-learning-for",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-15T09:15:08.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.21554v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Instead of learning a mapping from instances to outcomes in the standard way, the key idea of pairwise difference learning (PDL) is to learn a function that takes two instances as input and predicts the difference between the respective outcomes. Given a function of this kind, predictions for a query instance are derived from every training example and then averaged. We propose a version of PDL…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}